BOB batches users' transactions and submits them to Ethereum, paying a share of the transaction fees to Ethereum validators (expense).

BOB is an L2 smart contract platform for decentralized applications on Ethereum. BOB was founded by Alexei Zamyatin and Dominik Harz and launched in 2024.

BOB team & investors own and govern the protocol. BOB's investors include Castle Island Ventures, IOSG Ventures, Coinbase Ventures, etc.

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BOB (Build on Bitcoin)

# BOB (Build on Bitcoin) Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique addresses that initiated transactions on the Bob blockchain within a 30-day rolling window. This metric provides insight into user engagement and adoption trends for the Bob Layer 2 blockchain. The calculation uses HyperLogLog probabilistic data structures for efficient distinct user counting across time periods and supports multiple dimensional breakdowns including chain-level, user type, and product-level analysis.

## 2. Data sources

- **Bob blockchain transaction records**: Raw transaction data from the Bob Layer 2 blockchain containing transaction metadata, timestamps, sender addresses, and transaction hashes
- **Chain availability metadata**: Data quality indicators used to filter results during periods when chain data may be incomplete or unreliable

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identifies unique user addresses and their transaction activity from raw Bob blockchain data
- **Logic**: Extracts transaction sender addresses (`from_address`) along with block timestamps and transaction hashes from Bob blockchain transaction records. Each transaction sender is classified as a "Transaction sender" user type and assigned to the "blockchains_l2" market sector. The component filters to only Bob chain transactions and standardizes timestamp formats for downstream processing.
- **Key fields**: `block_timestamp` for timing analysis, `from_address` as the user identifier, `transaction_hash` for transaction uniqueness, and classification fields like `user_type` and `market_sector_id`

### Component B: Daily active user calculation
- **Purpose**: Calculates daily active users (DAU) using HyperLogLog sketches for efficient distinct counting
- **Logic**: Groups user addresses by day using timestamp truncation to day boundaries. For each day, creates HyperLogLog sketches of distinct user addresses grouped by dimensions including chain, user type, and product. The HyperLogLog approach enables efficient approximate distinct counting that can be merged across time periods. Applies incremental processing to only recalculate recent periods when new data arrives.
- **Key fields**: `timestamp` truncated to day granularity, `user_address` for distinct counting, dimensional grouping fields (`chain`, `user_type`, `product_id`), and `user_dau` containing the HyperLogLog sketch

### Component C: Rolling window creation
- **Purpose**: Creates a 30-day rolling window structure for MAU calculation using a datespine approach
- **Logic**: Generates a continuous date spine covering all days in the analysis period, then creates join relationships between each date and the 29 preceding days to form 30-day windows. This ensures consistent MAU calculation even when some days have no user activity. The datespine prevents gaps in time series data that could skew rolling window calculations.
- **Key fields**: `timestamp` for the window end date and `joinday` for each day within the 30-day lookback period

### Component D: MAU window aggregation
- **Purpose**: Combines daily HyperLogLog sketches across 30-day rolling windows to calculate MAU
- **Logic**: Joins the rolling window structure with daily HyperLogLog data to collect all user sketches within each 30-day period. Merges HyperLogLog sketches across the 30-day window using `hll_count.merge_partial` to combine probabilistic distinct counts. This approach maintains accuracy while handling the computational complexity of distinct counting across large time windows.
- **Key fields**: `timestamp` for the MAU calculation date, dimensional fields (`chain_id`, `user_type`, `product_id`), and merged `hll` sketches

### Component E: Dimensional breakdowns
- **Purpose**: Calculates MAU across multiple business dimensions for comprehensive analysis
- **Logic**: Creates separate MAU calculations broken down by project level (all dimensions combined), chain level, user type level, and product level. Each breakdown merges HyperLogLog sketches within the appropriate grouping and extracts final distinct counts using `hll_count.extract`. Unused dimensions are set to null to maintain consistent schema across breakdowns.
- **Key fields**: `dimensions` field indicating the breakdown type, relevant dimensional fields, and extracted `user_mau` counts

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a comprehensive MAU dataset with multiple analysis perspectives
- **Time aggregation**: Uses 30-day rolling windows with daily granularity, ensuring each date reflects MAU for the preceding 30-day period including that date
- **Missing data handling**: Applies standardized metric formatting through the `finalize_metric_user_mau` macro, which fills missing time periods with zero values to maintain complete time series
- **Final calculation**: The MAU value represents the approximate distinct count of user addresses that initiated at least one transaction within the 30-day rolling window, calculated using HyperLogLog probabilistic counting for computational efficiency

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Unique revenue-generating users over a rolling 30-day window.

BOB (Build on Bitcoin)BOB

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown